Allianz DataViz Challenge

library(plotly)
library(data.table)
library(tidyr)
library(knitr)
library(heatmaply)

Preprocessing

• Load data file
• rename genres for better readability
  • “Religion, Spirituality & New Age” to “Religion”
  • “Science.fiction” to “SciFi”
  • “Action.and.Adventure” to “Action”

All genres:

 [1] "Satire"        "SciFi"         "Drama"         "Action"       
 [5] "Romance"       "Mystery"       "Horror"        "Self.help"    
 [9] "Health"        "Guide"         "Travel"        "Children.s"   
[13] "Religion"      "Science"       "History"       "Math"         
[17] "Anthology"     "Poetry"        "Encyclopedias" "Dictionaries" 
[21] "Comics"        "Art"           "Cookbooks"     "Diaries"      
[25] "Journals"     

• Check if upper and lower triangle identical

[1] TRUE

• Transform to long and tidy data.table

head(books_dt)

• Average number of genres per customer

[1] 2.332187

First ideas

Show me everything!

• Romance, SciFi, Action, History are most bought
• bought-together clusters:
  • Romance, SciFi, Action, History
  • Dictionaries and Comics
  • Math and Poetry
• Mystery is an outlier

Most bought genre

Best pairs

• mostly combinations of most bought genres

Special genres

Hypothesis

• If a customer buys more than 2 genres, he is recorded in more than 1 off-diagonal entry –> (2*diagonal - colSum) < 0
• If a genre is bought more often alone than in triplets (or higher): (2*diagonal - colSum) > 0

Look for customers that buy only one genre

• Compare column sum and 2*diagonal value
• generate table with {genreA=<genre>, genreB=NA, customers={2*diagonal-colSum}}

Error in eval(expr, envir, enclos) : object 'diag_diff' not found